# Copyright (c) Meta Platforms, Inc. and affiliates.
# This software may be used and distributed according to the terms of the Llama 2 Community License Agreement.

import json
import os
import sys
import time
from pathlib import Path
from typing import List, Literal, Optional, Tuple, TypedDict

import torch
import torch.nn.functional as F
from fairscale.nn.model_parallel.initialize import (
    get_model_parallel_rank,
    initialize_model_parallel,
    model_parallel_is_initialized,
)

from llama.model import ModelArgs, Transformer
from llama.tokenizer import Tokenizer

Role = Literal["system", "user", "assistant"]


class Message(TypedDict):
    role: Role
    content: str


class CompletionPrediction(TypedDict, total=False):
    generation: str
    tokens: List[str]  # not required
    logprobs: List[float]  # not required


class ChatPrediction(TypedDict, total=False):
    generation: Message
    tokens: List[str]  # not required
    logprobs: List[float]  # not required


Dialog = List[Message]

B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"


class Llama:
    @staticmethod
    def build(
        ckpt_dir: str,
        tokenizer_path: str,
        max_seq_len: int,
        max_batch_size: int,
        model_parallel_size: Optional[int] = None,
    ) -> "Llama":
        if not torch.distributed.is_initialized():
            torch.distributed.init_process_group("nccl")
        if not model_parallel_is_initialized():
            if model_parallel_size is None:
                model_parallel_size = int(os.environ.get("WORLD_SIZE", 1))
            initialize_model_parallel(model_parallel_size)

        local_rank = int(os.environ.get("LOCAL_RANK", 0))
        torch.cuda.set_device(local_rank)

        # seed must be the same in all processes
        torch.manual_seed(1)

        if local_rank > 0:
            sys.stdout = open(os.devnull, "w")

        start_time = time.time()
        checkpoints = sorted(Path(ckpt_dir).glob("*.pth"))
        assert len(checkpoints) > 0, f"no checkpoint files found in {ckpt_dir}"
        assert model_parallel_size == len(
            checkpoints
        ), f"Loading a checkpoint for MP={len(checkpoints)} but world size is {model_parallel_size}"
        ckpt_path = checkpoints[get_model_parallel_rank()]
        checkpoint = torch.load(ckpt_path, map_location="cpu")
        with open(Path(ckpt_dir) / "params.json", "r") as f:
            params = json.loads(f.read())

        model_args: ModelArgs = ModelArgs(
            max_seq_len=max_seq_len,
            max_batch_size=max_batch_size,
            **params,
        )
        tokenizer = Tokenizer(model_path=tokenizer_path)
        model_args.vocab_size = tokenizer.n_words
        torch.set_default_tensor_type(torch.cuda.HalfTensor)
        model = Transformer(model_args)
        model.load_state_dict(checkpoint, strict=False)
        print(f"Loaded in {time.time() - start_time:.2f} seconds")

        return Llama(model, tokenizer)

    def __init__(self, model: Transformer, tokenizer: Tokenizer):
        self.model = model
        self.tokenizer = tokenizer

    @torch.inference_mode()
    def generate(
        self,
        prompt_tokens: List[List[int]],
        max_gen_len: int,
        temperature: float = 0.6,
        top_p: float = 0.9,
        logprobs: bool = False,
        echo: bool = False,
    ) -> Tuple[List[List[int]], Optional[List[List[float]]]]:
        params = self.model.params #模型参数
        bsz = len(prompt_tokens)   #提示token的组数
        assert bsz <= params.max_batch_size, (bsz, params.max_batch_size)

        min_prompt_len = min(len(t) for t in prompt_tokens) # 提示句子中最短的提示长度
        max_prompt_len = max(len(t) for t in prompt_tokens) # 提示句子中最长的提示长度
        assert max_prompt_len <= params.max_seq_len
        total_len = min(params.max_seq_len, max_gen_len + max_prompt_len) #最终要生成字总长度

        pad_id = self.tokenizer.pad_id #填充字
        # 生成一个shape 为(提示token的组数,total_len) 初始字符为pad_id的tokens
        tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device="cuda")
        for k, t in enumerate(prompt_tokens):
            tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device="cuda") # 挨个取出prompt中的每个句子 放到tokens中
        if logprobs:
            token_logprobs = torch.zeros_like(tokens, dtype=torch.float)  # 定义一个跟tokens尺寸一致的概率token_logprobs

        prev_pos = 0
        eos_reached = torch.tensor([False] * bsz, device="cuda") # 用于判断prompt中的每个句子是否已经处理完成
        input_text_mask = tokens != pad_id #mask 标记那些不是填充字的地方
        for cur_pos in range(min_prompt_len, total_len):
            logits = self.model.forward(tokens[:, prev_pos:cur_pos], prev_pos) # 以每个句子中的[prev_pos:cur_pos]部分作为输入去推理
            if logprobs:
                # 如果开启了计算概率，就会把当前输出的序列logits，与原始提示中的序列右移一位之后
                token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
                    input=logits.transpose(1, 2),
                    target=tokens[:, prev_pos + 1 : cur_pos + 1], #shape=(bst,cur_pos-prev_pos)
                    reduction="none",
                    ignore_index=pad_id, #这里需要注意一下，ignore_index参数的作用是忽略target中为pad_id所对应的logits分量
                                        #也就说当target右移到了pad_id，那么他与logits计算的loss不对整体loss产生影响，也就是你预测的是啥就是啥
                                        #target也不知道正确答案了
                )
            if temperature > 0:
                probs = torch.softmax(logits[:, -1] / temperature, dim=-1) #带温度系数的softmax
                next_token = sample_top_p(probs, top_p) #按sample_top_p的方式取next_token
            else:
                next_token = torch.argmax(logits[:, -1], dim=-1) #之间取概率最大的next_token

            next_token = next_token.reshape(-1)
            # only replace token if prompt has already been generated
            next_token = torch.where(
                input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token
            )
            tokens[:, cur_pos] = next_token
            eos_reached |= (~input_text_mask[:, cur_pos]) & (
                next_token == self.tokenizer.eos_id
            )
            prev_pos = cur_pos
            if all(eos_reached):
                break

        if logprobs:
            token_logprobs = token_logprobs.tolist()
        out_tokens, out_logprobs = [], []
        for i, toks in enumerate(tokens.tolist()):
            # cut to max gen len
            start = 0 if echo else len(prompt_tokens[i])
            toks = toks[start : len(prompt_tokens[i]) + max_gen_len]
            probs = None
            if logprobs:
                probs = token_logprobs[i][start : len(prompt_tokens[i]) + max_gen_len]
            # cut to eos tok if any
            if self.tokenizer.eos_id in toks:
                eos_idx = toks.index(self.tokenizer.eos_id)
                toks = toks[:eos_idx]
                probs = probs[:eos_idx] if logprobs else None
            out_tokens.append(toks)
            out_logprobs.append(probs)
        return (out_tokens, out_logprobs if logprobs else None)

    def text_completion(
        self,
        prompts: List[str],
        temperature: float = 0.6,
        top_p: float = 0.9,
        max_gen_len: Optional[int] = None,
        logprobs: bool = False,
        echo: bool = False,
    ) -> List[CompletionPrediction]:
        if max_gen_len is None:
            max_gen_len = self.model.params.max_seq_len - 1
        prompt_tokens = [self.tokenizer.encode(x, bos=True, eos=False) for x in prompts]
        generation_tokens, generation_logprobs = self.generate(
            prompt_tokens=prompt_tokens,
            max_gen_len=max_gen_len,
            temperature=temperature,
            top_p=top_p,
            logprobs=logprobs,
            echo=echo,
        )
        if logprobs:
            return [
                {
                    "generation": self.tokenizer.decode(t),
                    "tokens": [self.tokenizer.decode(x) for x in t],
                    "logprobs": logprobs_i,
                }
                for t, logprobs_i in zip(generation_tokens, generation_logprobs)
            ]
        return [{"generation": self.tokenizer.decode(t)} for t in generation_tokens]

    def chat_completion(
        self,
        dialogs: List[Dialog],
        temperature: float = 0.6,
        top_p: float = 0.9,
        max_gen_len: Optional[int] = None,
        logprobs: bool = False,
    ) -> List[ChatPrediction]:
        if max_gen_len is None:
            max_gen_len = self.model.params.max_seq_len - 1
        prompt_tokens = []
        for dialog in dialogs:
            if dialog[0]["role"] == "system":
                dialog = [
                    {
                        "role": dialog[1]["role"],
                        "content": B_SYS
                        + dialog[0]["content"]
                        + E_SYS
                        + dialog[1]["content"],
                    }
                ] + dialog[2:]
            assert all([msg["role"] == "user" for msg in dialog[::2]]) and all(
                [msg["role"] == "assistant" for msg in dialog[1::2]]
            ), (
                "model only supports 'system', 'user' and 'assistant' roles, "
                "starting with 'system', then 'user' and alternating (u/a/u/a/u...)"
            )
            dialog_tokens: List[int] = sum(
                [
                    self.tokenizer.encode(
                        f"{B_INST} {(prompt['content']).strip()} {E_INST} {(answer['content']).strip()} ",
                        bos=True,
                        eos=True,
                    )
                    for prompt, answer in zip(
                        dialog[::2],
                        dialog[1::2],
                    )
                ],
                [],
            )
            assert (
                dialog[-1]["role"] == "user"
            ), f"Last message must be from user, got {dialog[-1]['role']}"
            dialog_tokens += self.tokenizer.encode(
                f"{B_INST} {(dialog[-1]['content']).strip()} {E_INST}",
                bos=True,
                eos=False,
            )
            prompt_tokens.append(dialog_tokens)

        generation_tokens, generation_logprobs = self.generate(
            prompt_tokens=prompt_tokens,
            max_gen_len=max_gen_len,
            temperature=temperature,
            top_p=top_p,
            logprobs=logprobs,
        )
        if logprobs:
            return [
                {
                    "generation": {
                        "role": "assistant",
                        "content": self.tokenizer.decode(t),
                    },
                    "tokens": [self.tokenizer.decode(x) for x in t],
                    "logprobs": logprobs_i,
                }
                for t, logprobs_i in zip(generation_tokens, generation_logprobs)
            ]
        return [
            {"generation": {"role": "assistant", "content": self.tokenizer.decode(t)}}
            for t in generation_tokens
        ]


def sample_top_p(probs, p):
    #从给定的概率分布中采样一个token，采样的方式是先对概率进行排序，然后计算累积概率，
    #然后选择累积概率小于p的部分，最后在这部分中随机选择一个token。
    probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True) #给定的概率降序排序
    probs_sum = torch.cumsum(probs_sort, dim=-1) #从第一个元素开始，依次将序列中的每个元素与前面所有元素的和相加得到的
    mask = probs_sum - probs_sort > p 
    probs_sort[mask] = 0.0 #将累计和减去当前值>p的地方全部置0,留下来的就是概率较大的
    probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True)) #归一化下
    next_token = torch.multinomial(probs_sort, num_samples=1) # 从归一化之后的样本抽取一个样本
    next_token = torch.gather(probs_idx, -1, next_token) #从原始probs_idx找到next_token所对应的index
    return next_token